class: center, middle, inverse, title-slide # Wrap-up ### Dr. D’Agostino McGowan --- layout: true <div class="my-footer"> <span> Dr. Lucy D'Agostino McGowan </span> </div> --- ## What have we learned? -- * Supervised learning techniques -- * Focused on **prediction** -- * **bias-variance trade-off** --- ## How do we ensure that we aren't overfitting? * We _tune_ using an estimate of the _test error_ and find the model that minimizes this -- .question[ How do we estimate the test error? ] * Cross-validation * Validation Set * A combination of both --- ## Types of models * Regression * Classification --- ## Regression * Linear Regression -- * Ridge * Lasso * Elastic net -- * Regression Trees -- * Ensemble Trees * Bagged Trees * Random Forest * Boosted Trees --- class: center, middle # tidymodels ```r library(tidymodels) ``` --- ## Linear Regression in R ```r *mod_spec <- linear_reg() %>% * set_engine("lm") mod_spec ``` ``` ## Linear Regression Model Specification (regression) ## ## Computational engine: lm ``` --- ## Ridge Regression in R ```r *mod_spec <- linear_reg(penalty = 10, mixture = 0) %>% set_engine("glmnet") mod_spec ``` ``` ## Linear Regression Model Specification (regression) ## ## Main Arguments: ## penalty = 10 ## mixture = 0 ## ## Computational engine: glmnet ``` --- ## Lasso in R ```r *mod_spec <- linear_reg(penalty = 10, mixture = 1) %>% set_engine("glmnet") mod_spec ``` ``` ## Linear Regression Model Specification (regression) ## ## Main Arguments: ## penalty = 10 ## mixture = 1 ## ## Computational engine: glmnet ``` --- ## Elastic net in R ```r *mod_spec <- linear_reg(penalty = 10, mixture = 0.5) %>% set_engine("glmnet") mod_spec ``` ``` ## Linear Regression Model Specification (regression) ## ## Main Arguments: ## penalty = 10 ## mixture = 0.5 ## ## Computational engine: glmnet ``` --- ## Regression Trees in R ```r *mod_spec <- decision_tree(mode = "regression") %>% set_engine("rpart") mod_spec ``` ``` ## Decision Tree Model Specification (regression) ## ## Computational engine: rpart ``` --- ## Bagging in R ```r *mod_spec <- rand_forest( * mode = "regression", * mtry = 10) %>% * set_engine("ranger") mod_spec ``` ``` ## Random Forest Model Specification (regression) ## ## Main Arguments: ## mtry = 10 ## ## Computational engine: ranger ``` --- ## Random Forest in R ```r *mod_spec <- rand_forest( * mode = "regression") %>% * set_engine("ranger") mod_spec ``` ``` ## Random Forest Model Specification (regression) ## ## Computational engine: ranger ``` --- ## Boosting in R ```r *mod_spec <- boost_tree( * mode = "regression") %>% * set_engine("xgboost") mod_spec ``` ``` ## Boosted Tree Model Specification (regression) ## ## Computational engine: xgboost ``` --- ## Classification * Logistic Regression -- * Ridge * Lasso * Elastic net -- * Classification Trees -- * Ensemble Trees * Bagged Trees * Random Forest * Boosted Trees --- ## Logistic Regression in R ```r *mod_spec <- logistic_reg() %>% * set_engine("glm") mod_spec ``` ``` ## Logistic Regression Model Specification (classification) ## ## Computational engine: glm ``` --- ## Ridge (Logistic) in R ```r *mod_spec <- logistic_reg(penalty = 10, mixture = 0) %>% set_engine("glmnet") mod_spec ``` ``` ## Logistic Regression Model Specification (classification) ## ## Main Arguments: ## penalty = 10 ## mixture = 0 ## ## Computational engine: glmnet ``` --- ## Lasso (Logistic) in R ```r *mod_spec <- logistic_reg(penalty = 10, mixture = 1) %>% set_engine("glmnet") mod_spec ``` ``` ## Logistic Regression Model Specification (classification) ## ## Main Arguments: ## penalty = 10 ## mixture = 1 ## ## Computational engine: glmnet ``` --- ## Elastic net (Logistic) in R ```r *mod_spec <- logistic_reg(penalty = 10, mixture = 0.5) %>% set_engine("glmnet") mod_spec ``` ``` ## Logistic Regression Model Specification (classification) ## ## Main Arguments: ## penalty = 10 ## mixture = 0.5 ## ## Computational engine: glmnet ``` --- ## Bagging in R ```r *mod_spec <- rand_forest( * mode = "classification", * mtry = 10) %>% * set_engine("ranger") mod_spec ``` ``` ## Random Forest Model Specification (classification) ## ## Main Arguments: ## mtry = 10 ## ## Computational engine: ranger ``` --- ## Random Forest in R ```r *mod_spec <- rand_forest( * mode = "classification") %>% * set_engine("ranger") mod_spec ``` ``` ## Random Forest Model Specification (classification) ## ## Computational engine: ranger ``` --- ## Boosting in R ```r *mod_spec <- boost_tree( * mode = "classification") %>% * set_engine("xgboost") mod_spec ``` ``` ## Boosted Tree Model Specification (classification) ## ## Computational engine: xgboost ```